DETAILED ACTION
This Action is responsive to Claims filed 04/09/2026.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
Claims 5, 7- 8, 11, 13, 15, 19, 21-22, and 25 were previously cancelled. Claims 4, 14, 16-18, 20, 23-24, and 26 have been cancelled. Claims 1-3, 6, 9-10, 12, and 27 are currently pending.
Response to Amendment
The amendments have overcome the Objections to informalities presented in the Office Action dated 01/16/2026.
Response to Arguments
Applicant's arguments, see Pages 10-11, filed 04/09/2026, in regards to the 35 U.S.C. 101 Rejection of Claims 1-4, 6, 9-10, 12, 14, 16-18, 20, 23-24, and 26-27 have been fully considered but they are not persuasive.
On Page 10 of the arguments, the Applicant merely argues that the claimed matrix manipulations or operations are not practically performed within the human mind or with the aid of pen and paper without supporting structure, implementation, or reasoning why a human mind is incapable of performing said matrix operations. The Examiner respectfully reminds the Applicant that MPEP 2106.05(a) addresses that an amount or complexity of data does not preclude a human mind from performing an abstract idea mental process step, if the BRI of the limitation is interpretable as such.
Given the interpretation of the matrix manipulation steps above, and the BRI of the claimed linear regression and dimension reduction model (a “model” may be a set of equations not necessarily rooted in a computing environment), and the generic recitation of the use of said models (generically “processing…” matrices with them), the Examiner submits an alleged specific improvement is rooted in the data manipulation performed as abstract idea mental process steps. The construction of a matrix is practically performed within a human mind or with the aid of pen and paper, obtaining feature vectors of by generically “using” the matrix is practically performed within a human mind or with the aid of pen and paper, and obtaining a prediction result based on the generic output of the two generically recited models is practically performed within the human mind or with the aid of pen and paper. The “processing…” of the data by the machine learning models amounts to instructions to apply, given the generality of the “processing…” and “using…” of said models. See the updated 101 Rejection below.
Applicant’s arguments, see Page 11, filed 04/09/2026, with respect to the 35 U.S.C. 102(a)(1) rejection(s) of claim(s) 1-3, 9-10, 12, 14, 16-17, 23-24, and 26-27 have been fully considered and are persuasive by the inclusion of limitations from the cancelled Claim 4. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made under 35 U.S.C. 103.
Applicant's arguments, see Pages 11-13, filed 04/09/2026, with respect to the 35 U.S.C. 103 rejection of Claims 4, 6, 18, and 20 have been fully considered but they are not persuasive.
On pages 12-13, The Applicant alleges that Yu does not read on the features of Claim 4 without addressing the mapping provided by the Examiner, or Examiner’s reference to Yu Equation 6, which is functionally identical to Applicant’s Specification paragraphs [0008]-[0010]. In the absence of tangible arguments against the teachings of Yu, the Examiner maintains the mapping from the Non-Final Office Action dated 01/16/2026 for the limitations brought into the independent Claims from the now-cancelled Claim 4. See the updated 35 U.S.C. 103 Rejection below.
Claim Rejections - 35 USC § 101
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-3, 6, 9-10, 12, and 27 rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more; and because the claims as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than the abstract idea, see Alice Corporation Pty. Ltd. v. CLS Bank International, et al, 573 U.S. (2014). In determining whether the claims are subject matter eligible, the Examiner applies the 2019 USPTO Patent Eligibility Guidelines. (2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, Jan. 7, 2019.)
Step 1:
Claims 1-3, 6, and 9-10 recite a method, which falls under the statutory category of a process. Claim 12 recites an apparatus, which falls under the statutory category of a machine. Claim 27 recites a non-transitory computer-readable storage medium, which falls under the statutory category of a manufacture.
Step 2A – Prong 1:
Claim 1 recites an abstract idea, law of nature, or natural phenomenon. The limitations of “constructing a heterogeneous matrix, wherein the heterogeneous matrix comprises a first matrix representing the similarity between each two medications in a set of medications, a second matrix representing a similarity between each two diseases in a set of diseases, and a third matrix representing a relevance degree between each medication in the set of medications and each disease in the set of diseases;”, “obtaining a feature vector of the each medication in the set of medications and a feature vector of the each disease in the set of diseases by using the heterogeneous matrix;”, “generating a transformation matrix by using the heterogeneous matrix and an unit matrix;”, “generating an embedded feature vector according to the transformation matrix, a degree matrix of the heterogeneous matrix and a feature matrix, wherein the feature matrix comprises the third matrix;”, “splitting the embedded feature vector into a medication embedded vector and a disease embedded vector;”, “generating the relevance degree matrix by using the medication embedded vector, a preset weight vector and the disease embedded vector;”, and “obtaining a predicted result of the relevance degree between the each medication and the each disease according to the first predicted value of the relevance degree and the second predicted value of the relevance degree between the each medication and the each disease.” under the broadest reasonable interpretation, cover a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper.
Constructing a matrix of values is practically performed within the human mind or with the aid of pen and paper. Obtaining a feature vector using the matrices is practically performed within the human mind or with the aid of pen and paper. Generating a relevance degree matrix via transformation matrix and feature vectors is practically performed within the human mind or with the aid of pen and paper. Obtaining a predicted result of a relevance degree is practically performed within the human mind or with the aid of pen and paper.
Step 2A – Prong 2:
The additional elements of claim 1 do not integrate the abstract idea into a judicial exception. The claim recites the additional elements “heterogenous matrix” is recognized as generic computer components recited at a high level of generality. Although they have and execute instructions to perform the abstract idea itself, this also does not serve to integrate the abstract idea into a practical application as it merely amounts to instructions to "apply it." (See MPEP 2106.04(d)(2) indicating mere instructions to apply an abstract idea does not amount to integrating the abstract idea into a practical application).
The additional elements of “machine learning model”, “feature vector”, “logistic regression model”, and “model for dimension reduction” are recognized as non-generic computer components, but are recited at a high level of generality and are found to generally link the abstract idea to a particular technological environment or field of use (See MPEP 2106.05(h)).
The additional elements of “medications” and “diseases” are recognized as non-generic computer components, but are recited at a high level of generality and are found to generally link the abstract idea to a particular technological environment or field of use (See MPEP 2106.05(h)).
The additional elements recited in the limitations “processing the feature vector of the each medication and the feature vector of the each disease by using a first machine learning model to obtain a first predicted value of the relevance degree between the each medication and the each disease, wherein the first machine learning model comprises a logistic regression model;” and “processing the heterogeneous matrix by using a second machine learning model to obtain a relevance degree matrix, wherein the relevance degree matrix comprises a second predicted value of the relevance degree between the each medication and the each disease, wherein the second machine learning model comprises a model for dimension reduction of the heterogenous matrix…;” amount to instructions to apply the data manipulation abstract idea mental process steps (See MPEP 2106.05(f)).
Step 2B:
The additional elements of claim 1 do not amount to more than the judicial exception. The only limitation on the performance of the described method is a limitation reciting “heterogenous matrix” These elements are insufficient to transform a judicial exception to a patentable invention because the recited elements are considered insignificant extra-solution activity (generic computer system, processing resources, links the judicial exception to a particular, respective, technological environment). The claim thus recites computing components only at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components; mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (see MPEP 2106.05(f)).
The additional elements of “machine learning model”, “feature vector”, “logistic regression model”, and “model for dimension reduction” are recognized as non-generic computer components, but are recited at a high level of generality and are found to generally link the abstract idea to a particular technological environment or field of use (See MPEP 2106.05(h)).
The additional elements of “medications” and “diseases” are recognized as non-generic computer components, but are recited at a high level of generality and are found to generally link the abstract idea to a particular technological environment or field of use (See MPEP 2106.05(h)).
The additional elements recited in the limitations “processing the feature vector of the each medication and the feature vector of the each disease by using a first machine learning model to obtain a first predicted value of the relevance degree between the each medication and the each disease, wherein the first machine learning model comprises a logistic regression model;” and “processing the heterogeneous matrix by using a second machine learning model to obtain a relevance degree matrix, wherein the relevance degree matrix comprises a second predicted value of the relevance degree between the each medication and the each disease, wherein the second machine learning model comprises a model for dimension reduction of the heterogenous matrix…;”amount to instructions to apply the data manipulation abstract idea mental process steps (See MPEP 2106.05(f)).
Taken alone or in ordered combination, these additional elements do not amount to significantly more than the above-identified abstract idea. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
For the reasons above, claim 1 is rejected as being directed to non-patentable subject matter under §101. This rejection applies equally to independent claims 12 and 27.
Claim 12 recites similar limitations to claim 1, with the exception of “An apparatus for predicting a relevance degree, comprising: a processor; and a memory coupled to the processor, storing program instructions which, when executed by the processor, cause the processor to” (generic computer components); therefore, both claims are similarly rejected.
Claim 27 recites similar limitations to claim 1, with the exception of “A non-transitory computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions that, when executed by a processor, cause the processor to:” (generic computer components); therefore, both claims are similarly rejected.
Dependent Claims:
Claim 2 recites refinements to how one would perform the abstract idea mental process step(s) of the independent claims.
Claim 3 recites refinements to the data types manipulated in the independent claims.
Claim 6 (claim 20) recites abstract idea mental process steps “generating…” and “generating…”.
Claim 9 (claim 23) recites abstract idea mental process steps “splicing…” and instructions to apply the “splicing…” step “processing…” (See MPEP 2106.05(f)).
Claim 10 (claim 24) recites abstract idea mental process steps “constructing…”, “constructing …”, “constructing …”, “constructing”, and “generating…”.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-3, 6, 9-10, 12, and 27 is/are rejected under 35 U.S.C. 103 as being unpatentable over Song et al. (Prediction of Drug-Related Diseases Through Integrating Pairwise Attributes and Neighbor Topological Structures, published 06/16/2021), hereinafter Song and Yu et al. (Predicting drug–disease associations through layer attention graph convolutional network, 2021), hereinafter Yu.
In regards to claim 1: The present invention claims: “A method for predicting a relevance degree, comprising: constructing a heterogeneous matrix, wherein the heterogeneous matrix comprises a first matrix representing the similarity between each two medications in a set of medications, a second matrix representing a similarity between each two diseases in a set of diseases, and a third matrix representing a relevance degree between each medication in the set of medications and each disease in the set of diseases;” Song teaches “The model framework, ANPred, has been proposed for drug-disease association prediction (as shown in Fig. 1).” (Page 2964) and “The construction of bi-layer drug-disease heterogeneous network and random walk sequence set. (a) Construct a bi-layer drug-disease heterogeneous network, where R and D are the drug similarity matrix and the disease similarity matrix respectively, and A is the drug-disease association matrix, (b) Neighbor node sequence set based on random walk.” (Figure 2, Page 2965). Section 2.2 details the formulation and construction of these matrices.
“obtaining a feature vector of the each medication in the set of medications and a feature vector of the each disease in the set of diseases by using the heterogeneous matrix;” Song Section 2.3, Page 2965, describes the formulation of a feature vector from the drug-drug similarity matrix and association matrix (F1) and the disease-disease similarity matrix and association matrix (F2), resulting in a feature vector matrix combining the two feature vectors (F).
“processing the feature vector of the each medication and the feature vector of the each disease by using a first machine learning model to obtain a first predicted value of the relevance degree between the each medication and the each disease, wherein the first machine learning model comprises a logistic regression model;” Song teaches “The neighbor topology representation of the drug ri obtained from the FAS framework of the drug is gri . The neighbor topology representation of the disease dj obtained from the FAS framework of the disease is gdj . The drug and disease node vectors were connected through the connection strategy to obtain the node pair neighbor topology matrix G (Fig. 1e). To obtain the neighbor topology vector of the deep drug-disease node pair, we feed the neighbor topology matrix G of the node pair to the convolutional neural network to obtain the output feature sn.” (Page 2968, Fig. 1e, the drug-drug features and disease-disease features are used to obtain one output by Fig. 1e’s CNN). Song makes reference to a logistic regression model being utilized in previous work (Page 2964, left column). Song also makes reference to the utilization of softmax before classification of the two models’ outputs (Page 2968, left column). The Examiner submits one of ordinary skill in the art would have been aware of the use of logistic regression models and/or output layers of a CNN model performing logistic regression (as softmax does) for this field based on Song’s citations.
“processing the heterogeneous matrix by using a second machine learning model to obtain a relevance degree matrix, wherein the relevance degree matrix comprises a second predicted value of the relevance degree between the each medication and the each disease, wherein the second machine learning model comprises a model for dimension reduction of the heterogenous matrix…” Song teaches “A CNN-based module was constructed for the embedding matrix F to obtain the attribute representation of node pair. First, in order to obtain the edge information of the node pair feature matrix F, we used a padding operation on the feature matrix F to obtain a new feature matrix…where nconv is the number of circles padded around the feature matrix F. Second, the feature matrix F0 was sent to the convolutional neural network to learn the attribute representation of the drug-disease node pair.“ (Page 2966, Fig. 1b, the drug-disease association matrix is learned for a second output by another CNN). Song also teaches “To learn the low-dimensional node pair attribute representation and node neighbor topology encoding used in the prediction. A convolutional neural network module (CNN) and a joint module based on a fully-connected autoencoder and skip-gram (FAS) were established.” (Page 2966, left column) and “Given the drug feature vector fri , a fully connected autoencoder was constructed to obtain important, low-dimensional node feature representations (Fig. 4).” (Page 2966, right column). The Examiner submits this dimensionality reduction of one of the models of Song reasonably reads on the generic recitation of “a model for dimension reduction”.
“and obtaining a predicted result of the relevance degree between the each medication and the each disease according to the first predicted value of the relevance degree and the second predicted value of the relevance degree between the each medication and the each disease.” Song teaches “The final predicted score is the weighted sum of sore1 and sore2 (Fig. 1f)” (Page 2968, Fig. 1f, the results of the two CNNs are combined into a weighted sum for the final result).
While Song teaches the above, Song fails to explicitly teach the various matrices recited in: “generating a transformation matrix by using the heterogeneous matrix and an unit matrix; generating an embedded feature vector according to the transformation matrix, a degree matrix of the heterogeneous matrix and a feature matrix, wherein the feature matrix comprises the third matrix;” However, Yu, in a similar field of endeavor of drug-disease associations, teaches a heterogenous matrix, normalization of the matrix (mapping to the use of a generally recited unit matrix) (Page 3), as well as the formation of a feature embedding used said matrices (Page 3, Method Architecture). It is noted that Equation 6 of Yu is functionally identical to the equation(s) recited in Applicant’s Specification paragraphs [0008]-[0010].
Yu teaches “the development of efficient and high-accuracy computational methods for predicting drug–disease associations is of great significance.” and “we propose a novel computational method named as layer attention graph convolutional network (LAGCN) for the drug–disease association prediction. Specifically, LAGCN first integrates the known drug–disease associations, drug–drug similarities and disease–disease similarities into a heterogeneous network, and applies the graph convolution operation to the network to learn the embeddings of drugs and diseases.” (Abstract). It would have been obvious to one of ordinary skill in the art at the time of the Applicant’s filing to combine the matrix manipulation steps of Yu in a system such as Song’s to facilitate efficiency and accuracy.
“splitting the embedded feature vector into a medication embedded vector and a disease embedded vector;” See above rejections relying on Song and Song Figure 3 for the feature vector being split into diseases and drug data.
“and generating the relevance degree matrix by using the medication embedded vector, a preset weight vector and the disease embedded vector.” See the above rejections relying on Song, as well as Song Page 2966, right column, for the relevance degree matrix and weight matrix.
In regards to claim 2: The present invention claims: “wherein the prediction result of the relevance degree between an ith medication and a jth disease is a weighted sum of the first predicted value of the relevance degree between the ith medication and the jth disease and the second predicted value of the relevance degree of the ith medication and the jth disease, 1<i<M, 1<j<N, M is a total number of the medications, and N is a total number of the diseases.” Song teaches “The final predicted score is the weighted sum of sore1 and sore2 (Fig. 1f)” (Page 2968, Fig. 1f, the results of the two CNNs are combined into a weighted sum for the final result).
In regards to claim 3: The present invention claims: “wherein: the feature vector of the ith medication respectively comprises the similarity between the ith medication and the each medication in the set of medications and the relevance degree between the fi medication and the each disease in the set of diseases; and the feature vector of the jth disease respectively comprises the relevance degree between the jth disease and the each disease in the set of diseases, and the similarity between the jth disease and the each medication in the set of medications.” Song Section 2.3 (Pages 2965-2966) details the formulation of the feature matrix(s) included how the features of a given drug include the similarity between it and other drugs and association(s) with diseases, and vice versa.
In regards to claim 6: The present invention claims: “wherein the generating the embedded feature vector comprises: generating a temporary feature vector according to the transformation matrix, the degree matrix of the heterogeneous matrix, the feature matrix and a first learnable weight;” See the above rejection of claim 4 for Equation 6 of Yu matching the Equations used in the Applicant’s Specification for the embedded feature vector and/or temporary feature vector. See also Yu Equations 9 and 10 and Page 4 for the use of trainable matrices (mapping to the use of multiple weights).
“and generating the embedded feature vector according to the transformation matrix, the degree matrix of the heterogeneous matrix, the temporary feature vector and a second learnable weight.” See above how a combination of Song and Yu reads on the formation of the feature embedding.
In regards to claim 9: While Song reads on claim 1, Song fails to explicitly teach “splicing the feature vector of the each medication and the feature vector of the each disease to obtain a splicing feature; and processing the splicing feature by using a first machine learning model to obtain the first predicted value of the relevance degree of the each medication and the each disease.” See Song Fig. 3, specifically item F, for drug and disease features being merged (mapping to the generic recitation of “spliced”) into a feature vector.
In regards to claim 10: The present invention claims: “wherein the constructing the heterogeneous matrix comprises: constructing a first matrix, wherein the first matrix comprises the similarity between the each two medications in the set of medications;” See Song Sections 2.2 and 2.3, particularly Fig. 2 R.
“constructing a second matrix, wherein the second matrix comprises the similarity between the each two diseases in the set of diseases;” See Song Sections 2.2 and 2.3, particularly Fig. 2 D.
“constructing a third matrix, wherein the third matrix comprises the relevance degree between the each medication in the set of medications and the each disease in the set of diseases; and generating the heterogeneous matrix by using the first matrix, the second matrix and the third matrix.” Song Sections 2.2 and 2.3, particularly Fig. 2 A as well as subsequent use of all three matrices in the formulation of the network and resultant feature matrices.
In regards to claim 12: Claim 12 recites similar limitation to Claim 1, with the exception of “An apparatus for predicting a relevance degree, comprising: a processor; and a memory coupled to the processor, storing program instructions which, when executed by the processor, cause the processor to” recited in Claim 12; therefore, both claims are similarly rejected.
In regards to claim 27: The present invention claims: “A non-transitory computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions that, when executed by a processor, cause the processor to…”; therefore, both Claim 27 and Claim 1 are similarly rejected.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GRIFFIN T BEAN whose telephone number is (703)756-1473. The examiner can normally be reached M - F 7:30 - 4:30.
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/GRIFFIN TANNER BEAN/Examiner, Art Unit 2121
/Li B. Zhen/Supervisory Patent Examiner, Art Unit 2121
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